def extract_single_dimension(self, variable, t_max):
print("Extracting variable '{}'.".format(variable.name))
if self.folders is None:
self.get_folders()
# noinspection PyUnusedLocal
data = [[] for i in range(t_max)]
for i in tqdm(self.stats.data["idx"]):
results = Results(economy_folder=self.folders[i])
for t in range(t_max):
data[t].append(results.data[variable.name][t])
print("Convert in array.")
variable.data = dict()
variable.data["mean"] = np.array(
[np.mean(data[t]) for t in range(t_max)])
variable.data["std"] = np.array(
[np.std(data[t]) for t in range(t_max)])
print("Write in pickle.")
variable.write()
print("Done.")
def drawC(ps, img, v=0, r=10):
ps = np.array(ps)
if ps.ndim == 1:
ps = np.array([ps])
rrcc = [sk.draw.circle(p[0], p[1], r) for p in ps]
rr, cc = [rr for rr, cc in rrcc], [cc for rr, cc in rrcc]
rr, cc = np.array(reduce(np.append, rr)), np.array(reduce(np.append, cc))
rr, cc = setMaxMin(rr, img.shape[0], 0), setMaxMin(cc, img.shape[1], 0)
img[rr, cc] = v
def ek(alpha, betas):
ca = np.cos(alpha)
sa = np.sin(alpha)
cbs = np.cos(betas)
sbs = np.sin(betas)
return np.array([[ca * cbs, -sbs, sa * cbs], [ca * sbs, cbs, sa * sbs],
[-sa, 0, ca]])
def rK(lK, beta):
cb = np.cos(beta)
sb = np.sin(beta)
rKx = cb * lK
rKy = 0
rKz = sb * lK
return np.array([rKx, rKy, rKz])
def reflection(xvals, p):
#position=p[0]; intensity=p[1]; slit=p[2]; stth=p[3]; background=p[4]; thickness=p[5]; absorption=p[6]
x0 = p[0]
i0 = p[1]
w = p[2]
theta = p[3]
ib = p[4]
thick = p[5]
u = p[6]
th = np.deg2rad(theta)
p0 = w * np.sin(th) / np.sin(2 * th)
out = []
for x in xvals:
if x < (x0 - p0):
val = ib
elif ((x >= (x0 - p0)) and (x < x0)):
val = i0 * ((x - x0 + p0) / u) - i0 * (np.sin(th) /
(2 * u * u)) * (1 - np.exp(
(-2 * u / np.sin(th)) *
(x - x0 + p0))) + ib
elif ((x >= x0) and (x < (x0 + p0))):
val = i0 * (np.sin(th) / (2 * u * u)) * (np.exp(
(-2 * u / np.sin(th)) * (x - x0 + p0)) - 2 * np.exp(
(-2 * u / np.sin(th)) *
(x - x0)) + 1) + i0 * ((x0 + p0 - x) / u) + ib
elif (x >= (x0 + p0)):
val = i0 * np.sin(th) / (
2 * u * u) * (np.exp(-2 * u * (x - x0 + p0) / np.sin(th)) -
2 * np.exp(-2 * u * (x - x0) / np.sin(th)) +
np.exp(-2 * u * (x - x0 - p0) / np.sin(th))) + ib
out = out + [val]
return np.array(out)
def orthogonal_proj(zfront, zback):
a = (zfront + zback) / (zfront - zback)
b = -2 * (zfront * zback) / (zfront - zback)
# -0.0001 added for numerical stability as suggested in:
# http://stackoverflow.com/questions/23840756
return np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, a, b],
[0, 0, -0.0001, zback]])
def rB(R, omegat):
cot = np.cos(np.radians(omegat))
sot = np.sin(np.radians(omegat))
rBx = cot * R
rBy = sot * R
rBz = 0
return np.array([rBx, rBy, rBz])
def walldirect(xvals, p, mode='eval'):
""" From Wang et al.
"""
try:
if mode == 'eval':
out = wall(xvals, p)
True
elif mode == 'params':
#out = ['cent', 'sigma', 'amp', 'const', 'slope']
out = [
'position', 'intensity', 'slit', 'theta', 'background',
'thickness', 'absorption'
]
elif mode == 'name':
out = "Wall_Direct"
elif mode == 'guess':
xmin, xmax, ymin, ymax = [
xvals.min(), xvals.max(),
p.min(), p.max()
]
out = [
xmin + (xmax - xmin) / 2.0, ymax - ymin, (xmax - xmin) / 6.0,
45.0, ymin, (xmax - xmin) / 3.0, 0.01
]
True
else:
out = []
except:
out = np.array([0] * len(p))
return out
def drawArc(self, centerxy, startxy, endxy):
v1 = np.array([startxy[0] - centerxy[0], startxy[1] - centerxy[1]])
v2 = np.array([endxy[0] - centerxy[0], endxy[1] - centerxy[1]])
cos夹角 = (v1[0] * v2[0] + v1[1] * v2[1]) / (np.sqrt(v1.dot(v1)) *
np.sqrt(v2.dot(v2)))
angle_between = np.arccos(cos夹角)
radius = np.sqrt(v1.dot(v1))
angle_start = np.arctan2(v1[1], v1[0])
angle_end = angle_start + angle_between
self.ctx.move_to(startxy[0], startxy[1])
self.ctx.arc(centerxy[0], centerxy[1], radius, angle_start, angle_end)
# self.ctx.arc_negative(centerxy[0], centerxy[1], radius, angle_start, angle_end)
return []
def voigt(x, p, mode='eval'):
"""Pearson Type VII
From Hutchings et al. 2005. Introduction to the characterisation of residual stress by neutron diffraction.2005. Page 160
Function:
:math:`f(x) = k + m*x + p_2 \exp\left(\\frac{-(x - p_0)^2}{2p_1^2}\\right)`
"""
try:
if mode == 'eval':
#cent=p[0];wid=p[1];amp=p[2];const=p[3];slope=p[4]
x0 = p[0]
ux = p[1]
H0 = p[2]
const = p[3]
slope = p[4]
m = p[5]
out = H0 * (1 + (x - x0)**2.0 /
((1.0 / m) *
(0.5 * ux)**2))**-(1.0 / m) + const + slope * x
elif mode == 'params':
out = ['cent', 'sigma', 'amp', 'const', 'slope', 'shape']
elif mode == 'name':
out = "Voigt"
elif mode == 'guess':
g = fitfuncs.peakguess(x, p)
out = [g[0], g[1], g[3], g[5], g[4], 0.5]
else:
out = []
except:
out = [0, 0, 0, 0, 0, 0]
return np.array(out)
def transmissionreverse(xvals, p, mode='eval'):
""" From Brand et al.
"""
try:
if mode == 'eval':
xvals = xvals * -1.0
prms = p.copy()
prms[0] = -prms[0]
out = transmission(xvals, prms)
True
elif mode == 'params':
#out = ['cent', 'sigma', 'amp', 'const', 'slope']
out = [
'position', 'intensity', 'slit', 'theta', 'background',
'thickness', 'absorption'
]
elif mode == 'name':
out = "Transmission_Reverse"
elif mode == 'guess':
xmin, xmax, ymin, ymax = [
xvals.min(), xvals.max(),
p.min(), p.max()
]
out = [
xmin + (xmax - xmin) / 2.0, ymax - ymin, (xmax - xmin) / 3.0,
45.0, ymin, 0.0, 0.01
]
True
else:
out = []
except:
out = np.array([0] * len(p))
return out
def getSkeletonPositions(player):
l = [getXyzw(player.SkeletonPositions[idd]) for idd in range(20)]+\
[getXyzw(player.get_position())]
sp = np.array(l)
sp[-1, -1] = 0
x = np.mean(sp[[jo.shoulderCenter, jo.hipCenter, jo.spine]], 0)
sp[:-1] -= x
return sp
def Marzmean(lK, Rg, alpha, betas, beta, delta):
pos = np.array([0, 0.5 * np.pi, np.pi, 1.5 * np.pi])
omegat = np.arange(0, 360, 1)
alpha, betas = orientation(lK, Rg, alpha, betas, beta, delta, omegat, pos)
return alpha, np.mean(
Marzsum(lK, Rg, alpha, betas, beta, delta, omegat,
pos)), Ftrminmax(lK, Rg, alpha, betas, beta, delta, omegat,
pos)
def Marzmean(lK, Rg, alpha, betas, beta, delta):
pos = np.array([0, 0.5 * np.pi, np.pi, 1.5 * np.pi])
omegat = np.arange(0, 360, 1)
a, b = orientation(lK, Rg, alpha, betas, beta, delta, omegat, pos)
alpha = a
betas = b
Ma = np.mean(Marzsum(lK, Rg, a, b, beta, delta, omegat, pos))
Mg = np.mean(Marzsum(lK, Rg, a, b, beta, delta, omegat, pos))
return alpha, betas, Ma, Mg
def getYx(player, img):
xy = [
skeleton_to_depth_image(sp, img.shape[1], img.shape[0])
for sp in player.SkeletonPositions
]
yx = np.array(xy)[..., [1, 0]].astype(int)
setMaxMin(yx[:, 0], VIDEO_WINSIZE[1], 0)
setMaxMin(yx[:, 1], VIDEO_WINSIZE[0], 0)
return yx
def show_prediction_result(image, label_image, clf):
size = (8, 8)
plt.figure(figsize=(15, 10))
plt.imshow(image, cmap='gray_r')
candidates = []
predictions = []
for region in regionprops(label_image):
# skip small images
# if region.area < 100:
# continue
# draw rectangle around segmented coins
minr, minc, maxr, maxc = region.bbox
# make regions square
maxwidth = np.max([maxr - minr, maxc - minc])
minr, maxr = int(0.5 * ((maxr + minr) - maxwidth)) - 3, int(0.5 * ((maxr + minr) + maxwidth)) + 3
minc, maxc = int(0.5 * ((maxc + minc) - maxwidth)) - 3, int(0.5 * ((maxc + minc) + maxwidth)) + 3
rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
fill=False, edgecolor='red', linewidth=2, alpha=0.2)
plt.gca().add_patch(rect)
# predict digit
candidate = image[minr:maxr, minc:maxc]
candidate = np.array(imresize(candidate, size), dtype=float)
# invert
# candidate = np.max(candidate) - candidate
# print im
# rescale to 16 in integer
candidate = (candidate - np.min(candidate))
if np.max(candidate) == 0:
continue
candidate /= np.max(candidate)
candidate[candidate < 0.2] = 0.0
candidate *= 16
candidate = np.array(candidate, dtype=int)
prediction = clf.predict(candidate.reshape(-1))
candidates.append(candidate)
predictions.append(prediction)
plt.text(minc - 10, minr - 10, "{}".format(prediction), fontsize=50)
plt.xticks([], [])
plt.yticks([], [])
plt.tight_layout()
plt.show()
return candidates, predictions
def show_prediction_result(image, label_image, clf):
size = (8, 8)
plt.figure(figsize=(15, 10))
plt.imshow(image, cmap='gray_r')
candidates = []
predictions = []
for region in regionprops(label_image):
# skip small images
# if region.area < 100:
# continue
# draw rectangle around segmented coins
minr, minc, maxr, maxc = region.bbox
# make regions square
maxwidth = np.max([maxr - minr, maxc - minc])
minr, maxr = int(0.5 * ((maxr + minr) - maxwidth)) - 3, int(0.5 * ((maxr + minr) + maxwidth)) + 3
minc, maxc = int(0.5 * ((maxc + minc) - maxwidth)) - 3, int(0.5 * ((maxc + minc) + maxwidth)) + 3
rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
fill=False, edgecolor='red', linewidth=2, alpha=0.2)
plt.gca().add_patch(rect)
# predict digit
candidate = image[minr:maxr, minc:maxc]
candidate = np.array(imresize(candidate, size), dtype=float)
# invert
# candidate = np.max(candidate) - candidate
# print im
# rescale to 16 in integer
candidate = (candidate - np.min(candidate))
if np.max(candidate) == 0:
continue
candidate /= np.max(candidate)
candidate[candidate < 0.2] = 0.0
candidate *= 16
candidate = np.array(candidate, dtype=int)
prediction = clf.predict(candidate.reshape(-1))
candidates.append(candidate)
predictions.append(prediction)
plt.text(minc - 10, minr - 10, "{}".format(prediction), fontsize=50)
plt.xticks([], [])
plt.yticks([], [])
plt.tight_layout()
plt.show()
return candidates, predictions
def plot_ui_curve(ax1, ax2, *arg, **kwarg):
u_data = voltage_data[kwarg['label']]
i_data = current_data[kwarg['label']]
ax1.plot(i_data, u_data, *arg, **kwarg)
ax1.set_xlabel('Current [A]')
ax1.set_ylabel('Voltage [V]')
voltage_to_compensate = np.array(u_data) - np.array(i_data) * R
ax2.plot(i_data, voltage_to_compensate, *arg, **kwarg)
ax2.set_xlabel('Current [A]')
ax2.set_ylabel('Voltage [V]')
print('#define LENGTH_OF_LUT ', len(i_data))
print('REAL lut_current_ctrl[LENGTH_OF_LUT] = {' +
', '.join(f'{el:g}' for el in i_data[::-1]) + '};')
print('REAL lut_voltage_ctrl[LENGTH_OF_LUT] = {' +
', '.join(f'{el:g}' for el in voltage_to_compensate[::-1]) + '};')
print()
def wall(xvals, parms):
#position=p[0]; intensity=p[1]; slit=p[2]; stth=p[3]; background=p[4]; thickness=p[5]; absorption=p[6]
midpoint = parms[0]
i0 = parms[1]
w = parms[2]
theta = parms[3]
ib = parms[4]
thick = parms[5]
u = parms[6]
midpoint = parms[0]
x0 = midpoint - thick / 2.0
th = np.deg2rad(theta)
p = w * np.cos(th)
Atot = w * w / np.sin(2.0 * th)
out = []
for x in xvals:
if (x <= (x0 - p)):
val = ib
#elif ((x0-p) >= (x-thick)) and ((x0+p) >= x): #Gauge volume is smaller than thickness
if ((x > (x0 - p)) and (x0 - p > (x - thick)) and (x <= x0)):
val = ib + i0 * ((x - (x0 - p)) * ((x -
(x0 - p)) * np.tan(th))) / Atot
elif ((x > x0) and (x <= x0 + p) and (x0 - p > (x - thick))):
val = ib + i0 * (Atot - (x0 + p - x) *
(x0 + p - x) * np.tan(th)) / Atot
elif (x0 - p > (x - thick)
and (x0 + p < x)): #Gauge volume is smaller than thickness
val = ib + i0
elif (x0 - p < (x - thick)
and (x0 + p > x)): #Gauge volume is larger than thickness
A2 = (x0 + p - x) * (x0 + p - x) * np.tan(th)
A3 = ((x - thick) - (x0 - p)) * (((x - thick) -
(x0 - p)) * np.tan(th))
val = ib + i0 * (Atot - A2 - A3) / Atot
elif ((x0 - p) < (x - thick)) and ((x0 + p) < x) and (x0 >=
(x - thick)):
A3 = ((x - thick) - (x0 - p)) * (((x - thick) -
(x0 - p)) * np.tan(th))
val = ib + i0 * (Atot - A3) / Atot
elif (x0 < (x - thick) and ((x0 + p >= (x - thick)))):
A2 = (x0 + p - (x - thick)) * ((x0 + p) - (x - thick)) * np.tan(th)
val = ib + i0 * (A2 / Atot)
elif (x0 + p < (x - thick)):
val = ib
#elif ((x0-p) < (x-thick)) and ((x0+p) >= x): #Gauge volume is larger than thickness
# A2 = (x0+p-x)*(x0+p-x)*np.tan(th)
# A3 = ((x-thick)-(x0-p))*(((x-thick)-(x0-p))*np.tan(th))
# val = ib + i0*(Atot-A2-A3)/Atot
out = out + [val]
return np.array(out)
def rA(lK, R, alpha, betas, beta, delta, omegat):
cb = np.cos(beta)
sb = np.sin(beta)
ca = np.cos(alpha)
sa = np.sin(alpha)
cbs = np.cos(betas)
sbs = np.sin(betas)
cotd = np.cos(np.radians(omegat) + delta)
sotd = np.sin(np.radians(omegat) + delta)
rAx = (ca * cbs * cotd - sbs * sotd) * R + cb * lK
rAy = (ca * sbs * cotd + cbs * sotd) * R
rAz = (-sa * cotd) * R + sb * lK
return np.array([rAx, rAy, rAz])
def __init__(self,colorname='red'):
self.id=1
self.start = self.stop = int(0)
self.position = self.fwhm = self.intensity = self.background = self.intensity_sum = self.intensity_area = self.counts = self.errustrain = float(0.0)
self.bgndfitparms = [0,0]
self.bgndyvals = np.array([]) #will contain the y values of the calculated background over the fit range
self.position_fix = self.fwhm_fix = self.intensity_fix = self.background_fix = False
self.position_stdev = self.fwhm_stdev = self.intensity_stdev = self.background_stdev = float(0.0)
self.chi2 = self.r2 = self.time = float(0.0)
self.position_valid = self.fwhm_valid = self.intensity_valid = self.background_valid = self.chi2_valid = self.r2_valid = True #Red for invalid, Black for valid
self.niter = int(0)
self.color = colorname
self.line = Line2D([], [], color = colorname, antialiased = False, alpha = 0.5, linestyle=":")
self.fittedline = Line2D([], [], color = colorname, antialiased = False)
self.bgrngline = Line2D([], [], color = colorname, antialiased = False, alpha = 0.5, linestyle=" ", marker='.', markersize=2.5)
self.bgline = Line2D([], [], color = colorname, antialiased = False, alpha = 0.5, linewidth = 0.5, linestyle="-")
self.diffline = Line2D([], [], color = colorname, antialiased = False, alpha = 0.9, linewidth = 0.2)
def transformCoords(self, points, deg_addTheta=0):
# This function takes in an nx2 array of coordinates of the form
# [x,y] and returns rotated and translated coordinates. The
# translation and rotation are described by obj.anchor_xy and
# obj.theta. The optional "addTheta" argument adds an
# additional angle of "addTheta" to the obj.theta attribute.
transCoords = []
for point in points:
# 旋转方向和eMach是反一下的，我是Park变换。
cosT = np.cos(self.theta + (deg_addTheta * np.pi / 180))
sinT = np.sin(self.theta + (deg_addTheta * np.pi / 180))
transCoords.append([
points[0] * cosT + points[1] * sinT,
points[0] * -sinT + points[1] * cosT
])
return np.array(transCoords)
def wallzscan(xvals, p):
#position=p[0]; intensity=p[1]; slit=p[2]; stth=p[3]; background=p[4]; thickness=p[5]; absorption=p[6]
midpoint = p[0]
i0 = p[1]
w = p[2]
theta = p[3]
ib = p[4]
thick = p[5]
u = p[6]
p0 = w / 2.0
x0 = midpoint - thick / 2.0
out = []
if w > thick:
maxbeam = thick
else:
maxbeam = w
for x in xvals:
x1 = x
x2 = x - thick
if (x1 <= (x0 - p0)) and (x1 <=
(x0 + p0)) and (x2 <=
(x0 - p0)) and (x2 <=
(x0 + p0)):
val = ib
elif ((x0 - p0) <= x1) and (
(x0 - p0) >= x2) and (x0 + p0) >= x1 and (x0 + p0) >= x2:
val = ib + i0 * (x1 - (x0 - p0)) / maxbeam
elif (x0 - p0) >= x2 and (x0 + p0) <= x1 and (x0 - p0) <= x1 and (
x0 + p0) >= x2:
val = ib + i0
elif (x0 - p0) <= x2 and (x0 + p0) >= x2 and (x0 + p0 <=
x1) and (x0 - p0) <= x1:
val = ib + i0 * ((x0 + p0) - x2) / maxbeam
elif (x0 - p0) <= x2 and (x0 + p0) <= x2 and (x0 - p0) <= x1 and (
x0 + p0) <= x1:
val = ib
elif (x0 - p0) <= x2 and (x0 + p0) >= x1 and (x0 - p0) <= x1 and (
x0 + p0) >= x2:
val = ib + i0
out = out + [val]
return np.array(out)
def transmission(xvals, parms):
#position=p[0]; intensity=p[1]; slit=p[2]; stth=p[3]; background=p[4]; thickness=p[5]; absorption=p[6]
#x0=p[0]; i0=p[1]; w=p[2]; theta=p[3]; ib=p[4]; thick=p[5]; u=p[6];
#th = np.deg2rad(theta)
#p0 = w * np.cos(th)/np.cos(2*th)
#out = []
#for x in xvals:
# if x < (x0 - p0):
# val = ib
# elif ((x>=x0-p0) and (x < x0)):
# val = i0*(x-x0+p0)*(x-x0+p0)+ib
# elif ((x>=x0) and (x<(x0+p0))):
# val = i0*(2.0*p0*p0-(x0+p0-x)*(x0+p0-x))+ib
# elif (x>=x0+p0):
# val=2*i0*p0*p0 +ib
# out = out + [val]
x0 = parms[0]
i0 = parms[1]
w = parms[2]
theta = parms[3]
ib = parms[4]
thick = parms[5]
u = parms[6]
th = np.deg2rad(theta)
p = w * np.cos(th)
Atot = w * w / np.sin(2.0 * th)
out = []
for x in xvals:
if (x <= (x0 - p)):
val = ib
elif ((x > (x0 - p)) and (x <= x0)):
val = ib + i0 * ((x - (x0 - p)) * ((x -
(x0 - p)) * np.tan(th))) / Atot
elif ((x > x0) and (x <= x0 + p)):
val = ib + i0 * (Atot - (x0 + p - x) *
(x0 + p - x) * np.tan(th)) / Atot
elif (x > x0 + p):
val = ib + i0
out = out + [val]
return np.array(out)
def zscan(xvals, p):
#position=p[0]; intensity=p[1]; slit=p[2]; stth=p[3]; background=p[4]; thickness=p[5]; absorption=p[6]
x0 = p[0]
i0 = p[1]
w = p[2]
theta = p[3]
ib = p[4]
thick = p[5]
u = p[6]
p0 = w / 2.0
#b=p[4]; m=p[1]; a=p[2]; x0=p[0]
#out = b+(m-b)/(1+np.exp(-a*(xvals-x0)))
out = []
for x in xvals:
if x < (x0 - p0):
val = ib
elif (x > (x0 - p0)) and (x < (x0 + p0)):
val = ib + i0 * (x - (x0 - p0)) / (2 * p0)
else:
val = ib + i0
out = out + [val]
return np.array(out)
def Gauss(x, p, mode='eval'):
"""Gaussian defined by amplitide
Function:
:math:`f(x) = k + m*x + p_2 \exp\left(\\frac{-(x - p_0)^2}{2p_1^2}\\right)`
"""
try:
if mode == 'eval':
cent = p[0]
wid = p[1]
amp = p[2]
const = p[3]
slope = p[4]
#out = const + amp * np.exp(-1.0 * (x - cent)**2 / (2 * wid**2))
#Inserted the 0.5* because we want FWHM out, not HWHM
#out = const + slope*x + amp * np.exp(-1.0 * (x - cent)**2 / (2 * (0.5*wid)**2))
conversion = 2.0 * np.sqrt(
2.0 * np.log(2)
) #Hutchings et al. Introduction to the characterisation of residual stress by neutron diffraction.2005. Page 159
#conversion = 2.0
out = const + slope * x + amp * np.exp(-1.0 * (x - cent)**2 /
(2 * (wid / conversion)**2))
elif mode == 'params':
out = ['cent', 'sigma', 'amp', 'const', 'slope']
elif mode == 'name':
out = "Gaussian"
elif mode == 'guess':
g = fitfuncs.peakguess(x, p)
out = [g[0], g[1] / (4 * np.log(2)), g[3], g[4], g[5]]
else:
out = []
except:
out = [0, 0, 0, 0, 0]
return np.array(out)
def get_sharp_rate(done_exp):
cap_list = [float(x[0]) for x in done_exp]
return_list = []
base_cap = float(done_exp[0][0])
for i in range(len(cap_list)):
if i == 0:
return_list.append(float(1.00))
else:
ri = (float(done_exp[i][0]) - float(done_exp[0][0])) / float(
done_exp[0][0])
return_list.append(ri)
std = float(np.array(return_list).std())
exp_portfolio = (float(done_exp[-1][0]) - float(done_exp[0][0])) / float(
done_exp[0][0])
exp_norisk = 0.04 * (5.0 / 12.0)
sharp_rate = (exp_portfolio - exp_norisk) / (std)
return sharp_rate, std
def __new__(cls, array_like, culture):
self = np.array(array_like).view(cls)
return self
def __new__(cls, convictions):
self = np.array(np.zeros(len(convictions), dtype=int)).view(cls)
return self
def get_matrix_of_agents_convictions(self):
return np.array([a.culture.convictions for a in self.agents])
def Marzmean0(lK, Rg, alpha, betas, beta, delta):
pos = np.array([0, 0.5 * np.pi, np.pi, 1.5 * np.pi])
omegat = np.arange(0, 360, 1)
return np.mean(Marzsum(lK, Rg, alpha, betas, beta, delta, omegat, pos))
from pylab import np
with open('ML Sample Data/RL 1k/TIME/Gaussian F=0.400 DC=50 A=0.50 TIME [2019-01-18 13_58_52.56].csv') as f:
arr = np.array([float(item) for item in f.readlines()])
print(arr)
